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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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7 results for “GLMMs”

Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sourcesstat.ML

Paper develops federated GLMM algorithms for analyzing hierarchical data.

problem Analyzing hierarchical data with non-independent observations in a federated setting.

method Developed two federated GLMM algorithms using Laplace and Gaussian Hermite approximations.

result Federated GLMM can handle hierarchical data and achieve comparable or superior performance.

federated learning GLMM mixed effects hierarchical data non-independence

2021-09-28abs ↗pdf ↗

Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) treesstat.ME

GLMM trees identify subgroups with different growth patterns in longitudinal data.

problem Identifying subgroups with distinct growth trajectories in longitudinal studies.

method Extended GLMM trees for longitudinal data.

result Extended GLMM trees outperform other methods in accuracy and speed.

GLMM trees growth curve models subgroup detection longitudinal data mixed effects models

2023-09-11abs ↗pdf ↗

Conjugate gradient methods for high-dimensional GLMMsstat.CO

Conjugate gradient methods improve efficiency for high-dimensional GLMMs.

problem Efficiency bottleneck in computing high-dimensional GLMM precision matrices.

method Combining spectral analysis and random graph theory with conjugate gradient methods.

result CG-based methods achieve linear scaling in cost with model parameters and observations.

GLMMs Conjugate Gradient High-dimensional Precision Matrices Random Graphs

2024-11-07abs ↗pdf ↗

Neural Generalized Mixed-Effects Modelsstat.ML

Neural models improve GLMMs for complex data.

problem Nonlinear relationships in grouped data.

method Replaced linear function with neural networks.

result Improved performance on synthetic and real-world data.

Neural Networks Mixed-Effects Models Generalized Linear Models Hierarchical Data Approximate Marginal Likelihood

2026-04-13abs ↗pdf ↗

Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Datastat.ML

Integrates neural encoders into GLMMs for multimodal data analysis.

problem Scalable Bayesian inference for GLMMs assumes low-dimensional tabular predictors and does not handle high-dimensional modalities.

method Jointly learns modality-specific neural encoders with GLMM objective, performs variance-corrected stochastic-gradient MCMC.

result Preserves interpretable fixed and random effects while scaling to large longitudinal datasets.

Bayesian inference Generalized Linear Mixed Models Neural Encoders Multimodal Data Uncertainty Quantification

2026-07-06abs ↗pdf ↗

Scalable and Calibrated Sampling for Bayesian Generalized Linear Mixed Model via Stochastic Gradient Markov Chain Monte Carlostat.CO

New method for accurate Bayesian inference in GLMMs for large-scale data.

problem Challenges in scalable Bayesian inference for GLMMs due to intractable marginal likelihood and high computational cost.

method Stochastic gradient Markov Chain Monte Carlo (SGMCMC) algorithm using Fisher's identity for gradient estimation.

result Accurate posterior inference in large-sample settings with properly calibrated uncertainty.

Bayesian inference Generalized linear mixed models Markov Chain Monte Carlo Stochastic gradient Electronic health records

2024-03-05abs ↗pdf ↗

A Coupled Markov Chain Approach to Credit Risk Modelingq-fin.RM

We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heurist…

2009-11-19abs ↗pdf ↗